code: def get_default_learner_class(self) -> Type["Learner"]:
      if self.framework_str == "tf2":
          from ray.rllib.core.testing.tf.bc_learner import BCTfLearner
  
          return BCTfLearner
      elif self.framework_str == "torch":
          from ray.rllib.core.testing.torch.bc_learner import (
              BCTorchLearner,
          )
  
          return BCTorchLearner
      else:
          raise ValueError(
              f"Unsupported framework: {self.framework_str}"
          )
  
file_path: ray/rllib/core/testing/testing_learner.py
imports: from typing import Any, DefaultDict, Dict, Mapping, Type
  import numpy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule, MultiAgentRLModuleSpec
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.typing import ModuleID, RLModuleSpec, TensorType
label_desc: Use image node_id 1 for calling the BaseTestingAlgorithmConfig obj's underlying member method code with example usage: obj.get_default_learner_class() and returns: BCTfLearner, BCTorchLearner